Road roughness is an important factor in determining the quality of a stretch of road. The International Roughness Index, a specific measure of road roughness, is widely used metric. However, in order to measure roughness, an accurate road profile must exist. To measure the roads, terrain profiling systems are commonly used. Modern systems based on laser scanners and inertial navigation systems (INS) are able to measure thousands of data points per seconds over a wide path. However, because of the subsystems in the profiling systems, they are susceptible to errors that reduce the accuracy of the measurements. Thus, both major subsystems - the laser and the navigation system - must be accurate and synchronized for the road to be correctly scanned. The sensors' mounting was investigated to ensure that the vehicle motion is accurately captured and accounted for, demonstrated in the Vehicle Terrain Performance Lab's (VTPL) Ford Explorer profilometer. Next, INS errors were addressed. These may include drift in the inertial measurement unit or errors due to poor reception with the global navigation satellite system. The solution to these errors was demonstrated through the VTPL's HMMWV profilometer. / Master of Science

Terrain surface measurement is an important tool in vehicle design work as well as pavement classification and health monitoring. �Non-deformable terrains are the primary excitation to vehicles traveling over it, and therefore it is important to be able to quantify these terrain surfaces. Knowledge of the terrain can be used in combination with vehicle models in order to predict force loads the vehicles would experience while driving over the terrain surface. �This is useful in vehicle design, as it can speed the design process through the use of simulation as opposed to prototype construction and durability testing. �Additionally, accurate terrain maps can be used by highway engineers and maintenance personnel to identify deterioration in road surface conditions for immediate correction. �Repeated measurements of terrain surfaces over an extended length of time can also allow for long term pavement health monitoring. Many systems have been designed to measure terrain surfaces, most of them historically single line profiles, with more modern equipment capable of capturing three dimensional measurements of the terrain surface. �These more modern systems are often constructed using a combination of various sensors which allow the system to measure the relative height of the terrain with respect to the terrain measurement system. �Additionally, these terrain measurement systems are also equipped with sensors which allow the system to be located in some global coordinate space, as well as the angular attitude of that system to be estimated. �Since all sensors return estimated values, with some uncertainty, the combination of a group of sensors serves to also combine their uncertainties, resulting in a system which is less precise than any of its individual components. �In order to predict the precision of the system, the individual probability densities of the components must be quantified, in some cases transformed, and finally combined in order to predict the system precision. �This thesis provides a proof-of-concept as to how such an evaluation of final precision can be performed. / Master of Science

In the field of modern automotive engineering, many researchers are focusing on the development of advanced vehicle control systems such as autonomous vehicle systems and Advanced Driver Assistance Systems (ADAS). Furthermore, Driver Assistance Systems (DAS) such as cruise control, Anti-Lock Braking Systems (ABS), and Electronic Stability Control (ESC) have become widely popular in the automotive industry. Therefore, vehicle control research attracts attention from both academia and industry, and has been an active area of vehicle research for over 30 years, resulting in impressive DAS contributions. Although current vehicle control systems have improved vehicle safety and performance, there is room for improvement for dealing with various situations. The objective of the research is to develop a predictive vehicle control system for improving vehicle safety and performance for autonomous vehicles and ADAS. In order to improve the vehicle control system, the proposed system utilizes information about the upcoming local driving environment such as terrain roughness, elevation grade, bank angle, curvature, and friction. The local driving environment is measured in advance with a terrain measurement system to provide terrain data. Furthermore, in order to obtain the information about road conditions that cannot be measured in advance, this work begins by analyzing the response measurements of a preceding vehicle. The response measurements of a preceding vehicle are acquired through Vehicle-to-Vehicle (V2V) or Vehicle-to-Infrastructure (V2I) communication. The identification method analyzes the response measurements of a preceding vehicle to estimate road data. The estimated road data or the pre-measured road data is used as the upcoming driving environment information for the developed vehicle control system. The metric that objectively quantifies vehicle performance, the Performance Margin, is developed to accomplish the control objectives in an efficient manner. The metric is used as a control reference input and continuously estimated to predict current and future vehicle performance. Next, the predictive control algorithm is developed based on the upcoming driving environment and the performance metric. The developed system predicts future vehicle dynamics states using the upcoming driving environment and the Performance Margin. If the algorithm detects the risks of future vehicle dynamics, the control system intervenes between the driver's input commands based on estimated future vehicle states. The developed control system maintains vehicle handling capabilities based on the results of the prediction by regulating the metric into an acceptable range. By these processes, the developed control system ensures that the vehicle maintains stability consistently, and improves vehicle performance for the near future even if there are undesirable and unexpected driving circumstances. To implement and evaluate the integrated systems of this work, the real-time driving simulator, which uses precise real-world driving environment data, has been developed for advanced high computational vehicle control systems. The developed vehicle control system is implemented in the driving simulator, and the results show that the proposed system is a clear improvement on autonomous vehicle systems and ADAS. / Ph. D. / In the field of modern automotive engineering, many researchers are focusing on the development of advanced vehicle control systems such as autonomous vehicle systems and Advanced Driver Assistance Systems (ADAS). Furthermore, cruise control, Anti-Lock Braking Systems, and Electronic Stability Controls have become widely popular in the automotive industry. Although vehicle control systems have improved vehicle safety and performance, there is still room for improvement for dealing with various situations. The objective of the research is to develop a predictive vehicle control system for improving vehicle safety and performance for autonomous vehicles and ADAS. In order to improve the vehicle control system, the proposed system utilizes information about the upcoming driving conditions such as road roughness, elevation grade, bank angle, and curvature. The driving environment is measured in advance with a terrain measurement system. Furthermore, in order to obtain the information about road conditions that cannot be measured in advance, this work begins by analyzing a preceding vehicle’s response to the road. The combined road data is used as the upcoming driving environment information. The measurement that indicates vehicle performance, the Performance Margin, is developed to accomplish the research objectives. It is used in the developed control system, which predicts future vehicle performance. If the system detects future risks, the control system will intervene to correct the driver’s input commands. By these processes, the developed system ensures that the vehicle maintains stability, and improves vehicle performance regardless of the upcoming and unexpected driving conditions. To implement and evaluate the proposed systems, a driving simulator has been developed. The results show that the proposed system is a clear improvement on autonomous vehicle systems and ADAS.